This is a rewrite of my County Cluster notebook - originally written in Python in Jupyter Notebook - using R. The code may not be pretty, buy my goal is simply to practice R and demonstrate basic competence.

Loading packages, setting random seed

library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ──────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.7     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1
── Conflicts ─────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(modelr)
library(maps)

Attaching package: ‘maps’

The following object is masked from ‘package:purrr’:

    map
library(mapproj)
library(htmlwidgets)
Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
set.seed(123)

Read in datasets

ed_data <- read_csv("../data/Education.csv", locale = locale(encoding = "Latin1"))
Rows: 3283 Columns: 47
── Column specification ─────────────────────────────────────────────────────
Delimiter: ","
chr  (2): State, Area name
dbl (25): FIPS Code, 2003 Rural-urban Continuum Code, 2003 Urban Influenc...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
pop_data <- read_csv("../data/PopulationEstimates.csv", locale = locale(encoding = "Latin1"))
Warning: One or more parsing issues, see `problems()` for details
Rows: 3274 Columns: 149
── Column specification ─────────────────────────────────────────────────────
Delimiter: ","
chr  (3): FIPS, State, Area_Name
dbl (57): Rural-urban_Continuum Code_2003, Rural-urban_Continuum Code_201...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
pov_data <- read_csv("../data/PovertyEstimates.csv", locale = locale(encoding = "Latin1"))
Rows: 3193 Columns: 34
── Column specification ─────────────────────────────────────────────────────
Delimiter: ","
chr  (2): Stabr, Area_name
dbl (17): FIPStxt, Rural-urban_Continuum_Code_2003, Urban_Influence_Code_...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
unm_data <- read_csv("../data/Unemployment.csv", locale = locale(encoding = "Latin1"))
Rows: 3275 Columns: 56
── Column specification ─────────────────────────────────────────────────────
Delimiter: ","
chr  (3): State, Area_name, Median_Household_Income_2018
dbl (17): FIPS, Rural_urban_continuum_code_2013, Urban_influence_code_201...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Taking a look at the education data

head(ed_data)

And the ed_data dimensions

dim(ed_data)
[1] 3283   47

Now looking at population data - it had a problem when reading in, we’ll have to interrogate that more later

head(pop_data)

pop_data dimensions

dim(pop_data)
[1] 3274  149

And now poverty data

head(pov_data)

poverty dimensions

dim(pov_data)
[1] 3193   34

And finally, unemployment data

head(unm_data)

and unemployment data dimensions

dim(unm_data)
[1] 3275   56

A quick look at the summaries of each

#summary(ed_data)
#summary(pop_data)
#summary(pov_data)
#summary(unm_data)

So it turns out that while summary() in R returns much the same information as .describe() does in pandas, it does it in a really ungainly format. I don’t think I’ll be doing that again. I’ve commented out the summaries to avoid clogging the notebook. At this point in pandas I listed out the data types in each dataframe, but here those have already been displayed on the head(). I also loaded in a shape file with the geometries of the counties, but this was ultimately only used for plotting and the maps package in R already covers US county mapping, so I won’t be using the shape file for this.

First, a tiny bit of housekeeping - making all the FIPS columns called just “FIPS”.

ed_data <- rename(ed_data, FIPS = "FIPS Code")
pov_data <- rename(pov_data, FIPS = FIPStxt)

Before joining, need to check that FIPS are actually unique

dim(distinct(select(ed_data, FIPS)))[1] == dim(ed_data)[1]
[1] TRUE
dim(distinct(select(pop_data, FIPS)))[1] == dim(pop_data)[1]
[1] TRUE
dim(distinct(select(pov_data, FIPS)))[1] == dim(pov_data)[1]
[1] TRUE
dim(distinct(select(unm_data, FIPS)))[1] == dim(unm_data)[1]
[1] TRUE

All good on that front. Of the four dataframes, three read in FIPS as dbl while pop_data read it in as chr. While the latter is more technically correct, I’ll convert pop_data FIPS to dbl so all four are on the same page when it comes to leading zeros and data types.

(pop_data <- mutate(pop_data, across(FIPS, as.double)))

And now to join them all on FIPS. And yes, the name of big_data is tongue-in-cheek.

big_data <- ed_data %>%
  full_join(pop_data, by = c("FIPS")) %>%
  full_join(pov_data, by = c("FIPS")) %>%
  full_join(unm_data, by = c("FIPS"))

Let’s take a look at the result

head(big_data)
dim(big_data)
[1] 3284  283

and now let’s see how many nulls there are

sort(colSums(is.na(big_data)))
                                                                    FIPS 
                                                                       1 
                                                                 State.x 
                                                                       1 
                                                               Area name 
                                                                       1 
                                                                   State 
                                                                       9 
                                                             Area_name.y 
                                                                       9 
                                Less than a high school diploma, 2014-18 
                                                                      11 
                                       High school diploma only, 2014-18 
                                                                      11 
                             Some college or associate's degree, 2014-18 
                                                                      11 
                                    Bachelor's degree or higher, 2014-18 
                                                                      11 
         Percent of adults with less than a high school diploma, 2014-18 
                                                                      11 
              Percent of adults with a high school diploma only, 2014-18 
                                                                      11 
Percent of adults completing some college or associate's degree, 2014-18 
                                                                      11 
           Percent of adults with a bachelor's degree or higher, 2014-18 
                                                                      11 
                                                                 State.y 
                                                                      11 
                                                               Area_Name 
                                                                      11 
                                                         CENSUS_2010_POP 
                                                                      11 
                                                     ESTIMATES_BASE_2010 
                                                                      11 
                                                       POP_ESTIMATE_2010 
                                                                      11 
                                                       POP_ESTIMATE_2011 
                                                                      11 
                                                       POP_ESTIMATE_2012 
                                                                      11 
                                                       POP_ESTIMATE_2013 
                                                                      11 
                                                       POP_ESTIMATE_2014 
                                                                      11 
                                                       POP_ESTIMATE_2015 
                                                                      11 
                                                       POP_ESTIMATE_2016 
                                                                      11 
                                                       POP_ESTIMATE_2017 
                                                                      11 
                                                       POP_ESTIMATE_2018 
                                                                      11 
                                   Less than a high school diploma, 2000 
                                                                      12 
                                          High school diploma only, 2000 
                                                                      12 
                                Some college or associate's degree, 2000 
                                                                      12 
                                       Bachelor's degree or higher, 2000 
                                                                      12 
            Percent of adults with less than a high school diploma, 2000 
                                                                      12 
                 Percent of adults with a high school diploma only, 2000 
                                                                      12 
   Percent of adults completing some college or associate's degree, 2000 
                                                                      12 
              Percent of adults with a bachelor's degree or higher, 2000 
                                                                      12 
                                               Civilian_labor_force_2010 
                                                                      12 
                                                           Employed_2010 
                                                                      12 
                                                         Unemployed_2010 
                                                                      12 
                                                  Unemployment_rate_2010 
                                                                      12 
                                               Civilian_labor_force_2011 
                                                                      12 
                                                           Employed_2011 
                                                                      12 
                                                         Unemployed_2011 
                                                                      12 
                                                  Unemployment_rate_2011 
                                                                      12 
                                               Civilian_labor_force_2012 
                                                                      12 
                                                           Employed_2012 
                                                                      12 
                                                         Unemployed_2012 
                                                                      12 
                                                  Unemployment_rate_2012 
                                                                      12 
                                               Civilian_labor_force_2013 
                                                                      12 
                                                           Employed_2013 
                                                                      12 
                                                         Unemployed_2013 
                                                                      12 
                                                  Unemployment_rate_2013 
                                                                      12 
                                               Civilian_labor_force_2014 
                                                                      12 
                                                           Employed_2014 
                                                                      12 
                                                         Unemployed_2014 
                                                                      12 
                                                  Unemployment_rate_2014 
                                                                      12 
                                               Civilian_labor_force_2015 
                                                                      12 
                                                           Employed_2015 
                                                                      12 
                                                         Unemployed_2015 
                                                                      12 
                                                  Unemployment_rate_2015 
                                                                      12 
                                               Civilian_labor_force_2016 
                                                                      12 
                                                           Employed_2016 
                                                                      12 
                                                         Unemployed_2016 
                                                                      12 
                                                  Unemployment_rate_2016 
                                                                      12 
                                               Civilian_labor_force_2017 
                                                                      12 
                                                           Employed_2017 
                                                                      12 
                                                         Unemployed_2017 
                                                                      12 
                                                  Unemployment_rate_2017 
                                                                      12 
                                               Civilian_labor_force_2018 
                                                                      12 
                                                           Employed_2018 
                                                                      12 
                                                         Unemployed_2018 
                                                                      12 
                                                  Unemployment_rate_2018 
                                                                      12 
                                   Less than a high school diploma, 1990 
                                                                      13 
                                          High school diploma only, 1990 
                                                                      13 
                                Some college or associate's degree, 1990 
                                                                      13 
                                       Bachelor's degree or higher, 1990 
                                                                      13 
            Percent of adults with less than a high school diploma, 1990 
                                                                      13 
                 Percent of adults with a high school diploma only, 1990 
                                                                      13 
              Percent of adults with a bachelor's degree or higher, 1990 
                                                                      13 
   Percent of adults completing some college or associate's degree, 1990 
                                                                      14 
                                               Civilian_labor_force_2007 
                                                                      14 
                                                           Employed_2007 
                                                                      14 
                                                         Unemployed_2007 
                                                                      14 
                                                  Unemployment_rate_2007 
                                                                      14 
                                               Civilian_labor_force_2008 
                                                                      14 
                                                           Employed_2008 
                                                                      14 
                                                         Unemployed_2008 
                                                                      14 
                                                  Unemployment_rate_2008 
                                                                      14 
                                               Civilian_labor_force_2009 
                                                                      14 
                                                           Employed_2009 
                                                                      14 
                                                         Unemployed_2009 
                                                                      14 
                                                  Unemployment_rate_2009 
                                                                      14 
                                   Less than a high school diploma, 1980 
                                                                      17 
                                          High school diploma only, 1980 
                                                                      17 
                                          Some college (1-3 years), 1980 
                                                                      17 
                                   Four years of college or higher, 1980 
                                                                      17 
            Percent of adults with less than a high school diploma, 1980 
                                                                      17 
                 Percent of adults with a high school diploma only, 1980 
                                                                      17 
             Percent of adults completing some college (1-3 years), 1980 
                                                                      17 
      Percent of adults completing four years of college or higher, 1980 
                                                                      17 
                                                              Metro_2013 
                                                                      62 
                                         2003 Rural-urban Continuum Code 
                                                                      63 
                                               2003 Urban Influence Code 
                                                                      63 
                                         2013 Rural-urban Continuum Code 
                                                                      63 
                                               2013 Urban Influence Code 
                                                                      63 
                                         Rural-urban_Continuum Code_2013 
                                                                      64 
                                             Urban_Influence_Code_2013.x 
                                                                      64 
                                         Rural_urban_continuum_code_2013 
                                                                      65 
                                               Urban_influence_code_2013 
                                                                      65 
                                         Rural-urban_Continuum Code_2003 
                                                                      69 
                                             Urban_Influence_Code_2003.x 
                                                                      69 
                                                          N_POP_CHG_2010 
                                                                      90 
                                                          N_POP_CHG_2011 
                                                                      90 
                                                          N_POP_CHG_2012 
                                                                      90 
                                                          N_POP_CHG_2013 
                                                                      90 
                                                          N_POP_CHG_2014 
                                                                      90 
                                                          N_POP_CHG_2015 
                                                                      90 
                                                          N_POP_CHG_2016 
                                                                      90 
                                                          N_POP_CHG_2017 
                                                                      90 
                                                          N_POP_CHG_2018 
                                                                      90 
                                                             Births_2010 
                                                                      90 
                                                             Births_2011 
                                                                      90 
                                                             Births_2012 
                                                                      90 
                                                             Births_2013 
                                                                      90 
                                                             Births_2014 
                                                                      90 
                                                             Births_2015 
                                                                      90 
                                                             Births_2016 
                                                                      90 
                                                             Births_2017 
                                                                      90 
                                                             Births_2018 
                                                                      90 
                                                             Deaths_2010 
                                                                      90 
                                                             Deaths_2011 
                                                                      90 
                                                             Deaths_2012 
                                                                      90 
                                                             Deaths_2013 
                                                                      90 
                                                             Deaths_2014 
                                                                      90 
                                                             Deaths_2015 
                                                                      90 
                                                             Deaths_2016 
                                                                      90 
                                                             Deaths_2017 
                                                                      90 
                                                             Deaths_2018 
                                                                      90 
                                                        NATURAL_INC_2010 
                                                                      90 
                                                        NATURAL_INC_2011 
                                                                      90 
                                                        NATURAL_INC_2012 
                                                                      90 
                                                        NATURAL_INC_2013 
                                                                      90 
                                                        NATURAL_INC_2014 
                                                                      90 
                                                        NATURAL_INC_2015 
                                                                      90 
                                                        NATURAL_INC_2016 
                                                                      90 
                                                        NATURAL_INC_2017 
                                                                      90 
                                                        NATURAL_INC_2018 
                                                                      90 
                                                  INTERNATIONAL_MIG_2010 
                                                                      90 
                                                  INTERNATIONAL_MIG_2011 
                                                                      90 
                                                  INTERNATIONAL_MIG_2012 
                                                                      90 
                                                  INTERNATIONAL_MIG_2013 
                                                                      90 
                                                  INTERNATIONAL_MIG_2014 
                                                                      90 
                                                  INTERNATIONAL_MIG_2015 
                                                                      90 
                                                  INTERNATIONAL_MIG_2016 
                                                                      90 
                                                  INTERNATIONAL_MIG_2017 
                                                                      90 
                                                  INTERNATIONAL_MIG_2018 
                                                                      90 
                                                       DOMESTIC_MIG_2010 
                                                                      90 
                                                       DOMESTIC_MIG_2011 
                                                                      90 
                                                       DOMESTIC_MIG_2012 
                                                                      90 
                                                       DOMESTIC_MIG_2013 
                                                                      90 
                                                       DOMESTIC_MIG_2014 
                                                                      90 
                                                       DOMESTIC_MIG_2015 
                                                                      90 
                                                       DOMESTIC_MIG_2016 
                                                                      90 
                                                       DOMESTIC_MIG_2017 
                                                                      90 
                                                       DOMESTIC_MIG_2018 
                                                                      90 
                                                            NET_MIG_2010 
                                                                      90 
                                                            NET_MIG_2011 
                                                                      90 
                                                            NET_MIG_2012 
                                                                      90 
                                                            NET_MIG_2013 
                                                                      90 
                                                            NET_MIG_2014 
                                                                      90 
                                                            NET_MIG_2015 
                                                                      90 
                                                            NET_MIG_2016 
                                                                      90 
                                                            NET_MIG_2017 
                                                                      90 
                                                            NET_MIG_2018 
                                                                      90 
                                                           RESIDUAL_2010 
                                                                      90 
                                                           RESIDUAL_2011 
                                                                      90 
                                                           RESIDUAL_2012 
                                                                      90 
                                                           RESIDUAL_2014 
                                                                      90 
                                                           RESIDUAL_2015 
                                                                      90 
                                                           RESIDUAL_2016 
                                                                      90 
                                                           RESIDUAL_2017 
                                                                      90 
                                                           RESIDUAL_2018 
                                                                      90 
                                                  GQ_ESTIMATES_BASE_2010 
                                                                      90 
                                                       GQ_ESTIMATES_2010 
                                                                      90 
                                                       GQ_ESTIMATES_2011 
                                                                      90 
                                                       GQ_ESTIMATES_2012 
                                                                      90 
                                                       GQ_ESTIMATES_2013 
                                                                      90 
                                                       GQ_ESTIMATES_2014 
                                                                      90 
                                                       GQ_ESTIMATES_2015 
                                                                      90 
                                                       GQ_ESTIMATES_2016 
                                                                      90 
                                                       GQ_ESTIMATES_2017 
                                                                      90 
                                                       GQ_ESTIMATES_2018 
                                                                      90 
                                                            R_birth_2011 
                                                                      91 
                                                            R_birth_2012 
                                                                      91 
                                                            R_birth_2013 
                                                                      91 
                                                            R_birth_2014 
                                                                      91 
                                                            R_birth_2015 
                                                                      91 
                                                            R_birth_2016 
                                                                      91 
                                                            R_birth_2017 
                                                                      91 
                                                            R_birth_2018 
                                                                      91 
                                                            R_death_2011 
                                                                      91 
                                                            R_death_2012 
                                                                      91 
                                                            R_death_2013 
                                                                      91 
                                                            R_death_2014 
                                                                      91 
                                                            R_death_2015 
                                                                      91 
                                                            R_death_2016 
                                                                      91 
                                                            R_death_2017 
                                                                      91 
                                                            R_death_2018 
                                                                      91 
                                                      R_NATURAL_INC_2011 
                                                                      91 
                                                      R_NATURAL_INC_2012 
                                                                      91 
                                                      R_NATURAL_INC_2013 
                                                                      91 
                                                      R_NATURAL_INC_2014 
                                                                      91 
                                                      R_NATURAL_INC_2015 
                                                                      91 
                                                      R_NATURAL_INC_2016 
                                                                      91 
                                                      R_NATURAL_INC_2017 
                                                                      91 
                                                      R_NATURAL_INC_2018 
                                                                      91 
                                                R_INTERNATIONAL_MIG_2011 
                                                                      91 
                                                R_INTERNATIONAL_MIG_2012 
                                                                      91 
                                                R_INTERNATIONAL_MIG_2013 
                                                                      91 
                                                R_INTERNATIONAL_MIG_2014 
                                                                      91 
                                                R_INTERNATIONAL_MIG_2015 
                                                                      91 
                                                R_INTERNATIONAL_MIG_2016 
                                                                      91 
                                                R_INTERNATIONAL_MIG_2017 
                                                                      91 
                                                R_INTERNATIONAL_MIG_2018 
                                                                      91 
                                                     R_DOMESTIC_MIG_2011 
                                                                      91 
                                                     R_DOMESTIC_MIG_2012 
                                                                      91 
                                                     R_DOMESTIC_MIG_2013 
                                                                      91 
                                                     R_DOMESTIC_MIG_2014 
                                                                      91 
                                                     R_DOMESTIC_MIG_2015 
                                                                      91 
                                                     R_DOMESTIC_MIG_2016 
                                                                      91 
                                                     R_DOMESTIC_MIG_2017 
                                                                      91 
                                                     R_DOMESTIC_MIG_2018 
                                                                      91 
                                                          R_NET_MIG_2011 
                                                                      91 
                                                          R_NET_MIG_2012 
                                                                      91 
                                                          R_NET_MIG_2013 
                                                                      91 
                                                          R_NET_MIG_2014 
                                                                      91 
                                                          R_NET_MIG_2015 
                                                                      91 
                                                          R_NET_MIG_2016 
                                                                      91 
                                                          R_NET_MIG_2017 
                                                                      91 
                                                          R_NET_MIG_2018 
                                                                      91 
                                                                   Stabr 
                                                                      91 
                                                             Area_name.x 
                                                                      91 
                                                             POVALL_2018 
                                                                      91 
                                                          CI90LBAll_2018 
                                                                      91 
                                                          CI90UBALL_2018 
                                                                      91 
                                                          PCTPOVALL_2018 
                                                                      91 
                                                         CI90LBALLP_2018 
                                                                      91 
                                                         CI90UBALLP_2018 
                                                                      91 
                                                             POV017_2018 
                                                                      91 
                                                          CI90LB017_2018 
                                                                      91 
                                                          CI90UB017_2018 
                                                                      91 
                                                          PCTPOV017_2018 
                                                                      91 
                                                         CI90LB017P_2018 
                                                                      91 
                                                         CI90UB017P_2018 
                                                                      91 
                                                             POV517_2018 
                                                                      91 
                                                          CI90LB517_2018 
                                                                      91 
                                                          CI90UB517_2018 
                                                                      91 
                                                          PCTPOV517_2018 
                                                                      91 
                                                         CI90LB517P_2018 
                                                                      91 
                                                         CI90UB517P_2018 
                                                                      91 
                                                           MEDHHINC_2018 
                                                                      91 
                                                          CI90LBINC_2018 
                                                                      91 
                                                          CI90UBINC_2018 
                                                                      91 
                                            Median_Household_Income_2018 
                                                                      91 
                                                           RESIDUAL_2013 
                                                                      92 
                               Med_HH_Income_Percent_of_State_Total_2018 
                                                                      92 
                                   Less than a high school diploma, 1970 
                                                                      98 
                                          High school diploma only, 1970 
                                                                      98 
                                          Some college (1-3 years), 1970 
                                                                      98 
                                   Four years of college or higher, 1970 
                                                                      98 
            Percent of adults with less than a high school diploma, 1970 
                                                                      98 
                 Percent of adults with a high school diploma only, 1970 
                                                                      98 
             Percent of adults completing some college (1-3 years), 1970 
                                                                      98 
      Percent of adults completing four years of college or higher, 1970 
                                                                      98 
                                                  Economic_typology_2015 
                                                                     142 
                                         Rural-urban_Continuum_Code_2013 
                                                                     143 
                                             Urban_Influence_Code_2013.y 
                                                                     143 
                                         Rural-urban_Continuum_Code_2003 
                                                                     148 
                                             Urban_Influence_Code_2003.y 
                                                                     148 
                                                              POV04_2018 
                                                                    3232 
                                                           CI90LB04_2018 
                                                                    3232 
                                                           CI90UB04_2018 
                                                                    3232 
                                                           PCTPOV04_2018 
                                                                    3232 
                                                          CI90LB04P_2018 
                                                                    3232 
                                                          CI90UB04P_2018 
                                                                    3232 

All columns have at least one null (I suspect this is due to the parsing problem from pop_data), a large number have <20, another significant group have 50-100, and a handful have 140-150 or outright majority nulls. On closer inspection, the outright majority null columns are state-level data which is not necessary for my analysis. Many of the other nulls may be the result of state-levels rows. Before anything else, I’ll save a copy of the full data

write_csv(big_data, "../data/outer_join_R.csv")

And now to clean it up. First to take out the mostly null columns

(big_data <- select(big_data, -c(POV04_2018, CI90LB04_2018, CI90UB04_2018, PCTPOV04_2018, CI90LB04P_2018, CI90UB04P_2018)))

Now the number of rows with nulls is much more reasonable

sum(!complete.cases(big_data))
[1] 159

Let’s see if my suspicion there’s a fully null row is correct

filter(big_data, apply(is.na(big_data), 1, all))

Getting rid of the fully null row

big_data <- filter(big_data, !apply(is.na(big_data), 1, all))

Now let’s look at the remaining rows with at least one null

filter(big_data, apply(is.na(big_data), 1, any))

That’s mostly Puerto Rico and Alaska, as well as the summary rows for states and the entire country. Here’s the rows with at least one null which DON’T meet any of the criteria I just listed.

filter(big_data, apply(is.na(big_data), 1, any) & !(State.x %in% c("AK", "PR")) & (FIPS %% 1000 != 0))

At this point in the original pandas notebook I broke to review external research to better understand what was going on with these counties (and county equivalents). I decided to narrow down to just columns I thought I might actually use before cleaning further, as the time component complicated cleaning some counties pretty severely.

(big_slice <- select(big_data, FIPS, State.x, "Area name", '2013 Rural-urban Continuum Code', '2013 Urban Influence Code', "Percent of adults with less than a high school diploma, 2014-18", 'Percent of adults with a high school diploma only, 2014-18', "Percent of adults completing some college or associate's degree, 2014-18", "Percent of adults with a bachelor's degree or higher, 2014-18", 'Economic_typology_2015', 'POP_ESTIMATE_2010', 'POP_ESTIMATE_2018', 'R_NATURAL_INC_2018', 'R_NET_MIG_2018', 'PCTPOVALL_2018', 'Unemployment_rate_2010', 'Unemployment_rate_2018', 'Median_Household_Income_2018'))
NA

Now I’ll get rid of Alaska, Hawaii, Puerto Rico, and the summary rows

(big_slice <- filter(big_slice, !(State.x %in% c("AK", "PR", "HI")) & (FIPS %% 1000 != 0)))

Now let’s see what nulls remain

filter(big_slice, apply(is.na(big_slice), 1, any))

Based on external research, I think I can drop all three of these without losing much

(big_slice <- drop_na(big_slice))

Now to finish the cleaning. I’ll make FIPS an int in R instead of a chr for mapping reasons which will come up later

(big_slice <- mutate(big_slice, across(FIPS, as.integer)))

The Rural-urban codes should be ints as well

(big_slice <- mutate(big_slice, across("2013 Rural-urban Continuum Code":"2013 Urban Influence Code", as.integer)))

Economic typology should really be a chr

(big_slice <- mutate(big_slice, across(Economic_typology_2015, as.character)))

Population estimates are currently doubles but should be integers

(big_slice <- mutate(big_slice, across(POP_ESTIMATE_2010:POP_ESTIMATE_2018, as.integer)))

And last but not least for type changes, median household income is currently a chr with dollar signs and thousand separators, neither of which we want

big_slice$Median_Household_Income_2018 <- str_replace_all(big_slice$Median_Household_Income_2018, "[[:punct:]/$]", "")
(big_slice <- mutate(big_slice, across(Median_Household_Income_2018, as.integer)))

Much better. Now I can create some new variables

(big_slice <- mutate(big_slice,
  Unemployment_change = Unemployment_rate_2018 - Unemployment_rate_2010,
  Population_percent_change = (POP_ESTIMATE_2018 - POP_ESTIMATE_2010) / POP_ESTIMATE_2010
))

And also one-hot-encode the Economic Typology

big_slice %>% 
  mutate(ones = 1) %>% 
  pivot_wider(
  names_from = Economic_typology_2015,
  values_from = ones,
  names_prefix = "Econ_Typology_",
  names_sort = TRUE,
  values_fill = 0,
  values_fn = as.integer)

Saving a copy of the modeling-ready data

write_csv(big_slice, "../data/processed_R.csv")

Let’s specify the X and y to use for modeling - more features may be used later

(X <- select(big_slice, 6, 13, 14, 17, 18, 19, 20))
y <- big_slice$PCTPOVALL_2018

Train test split

train_len <- as.integer(0.75 * dim(X)[1])
train_test_split <- sample(c(rep(0, train_len), rep(1, dim(X)[1] - train_len)))
X_train <- X[train_test_split == 0, ]
X_test <- X[train_test_split == 1, ]
y_train <- y[train_test_split == 0]
y_test <- y[train_test_split == 1]

Scaling the data

X_train_sc <- scale(X_train)

Can we get that scale back?

train_scale <- attributes(X_train_sc)$`scaled:scale`
train_centers <- attributes(X_train_sc)$`scaled:center`

And now to apply that to X_test

X_test_sc <- as_tibble(scale(X_test, center = train_centers, scale = train_scale))
X_sc <- as_tibble(scale(X, center = train_centers, scale = train_scale))
X_train_sc <- as_tibble(X_train_sc)

Now a linear model

linreg <- lm(y_train ~ Unemployment_rate_2018 + `Percent of adults with less than a high school diploma, 2014-18` + R_NATURAL_INC_2018 + R_NET_MIG_2018 + Median_Household_Income_2018 + Unemployment_change + Population_percent_change, data = X_train_sc)

Looking at the model summary - note that the R2 isn’t great, but not bad at all

summary(linreg)

Call:
lm(formula = y_train ~ Unemployment_rate_2018 + `Percent of adults with less than a high school diploma, 2014-18` + 
    R_NATURAL_INC_2018 + R_NET_MIG_2018 + Median_Household_Income_2018 + 
    Unemployment_change + Population_percent_change, data = X_train_sc)

Residuals:
     Min       1Q   Median       3Q      Max 
-16.0391  -1.8902  -0.4046   1.3001  24.0312 

Coefficients:
                                                                  Estimate
(Intercept)                                                       15.19936
Unemployment_rate_2018                                             1.26757
`Percent of adults with less than a high school diploma, 2014-18`  1.69539
R_NATURAL_INC_2018                                                 0.28640
R_NET_MIG_2018                                                    -0.60838
Median_Household_Income_2018                                      -3.43256
Unemployment_change                                               -0.11910
Population_percent_change                                          0.43550
                                                                  Std. Error
(Intercept)                                                          0.06619
Unemployment_rate_2018                                               0.07794
`Percent of adults with less than a high school diploma, 2014-18`    0.08450
R_NATURAL_INC_2018                                                   0.08785
R_NET_MIG_2018                                                       0.09590
Median_Household_Income_2018                                         0.09375
Unemployment_change                                                  0.07581
Population_percent_change                                            0.11117
                                                                  t value
(Intercept)                                                       229.619
Unemployment_rate_2018                                             16.263
`Percent of adults with less than a high school diploma, 2014-18`  20.063
R_NATURAL_INC_2018                                                  3.260
R_NET_MIG_2018                                                     -6.344
Median_Household_Income_2018                                      -36.613
Unemployment_change                                                -1.571
Population_percent_change                                           3.917
                                                                  Pr(>|t|)
(Intercept)                                                        < 2e-16
Unemployment_rate_2018                                             < 2e-16
`Percent of adults with less than a high school diploma, 2014-18`  < 2e-16
R_NATURAL_INC_2018                                                 0.00113
R_NET_MIG_2018                                                    2.68e-10
Median_Household_Income_2018                                       < 2e-16
Unemployment_change                                                0.11630
Population_percent_change                                         9.21e-05
                                                                     
(Intercept)                                                       ***
Unemployment_rate_2018                                            ***
`Percent of adults with less than a high school diploma, 2014-18` ***
R_NATURAL_INC_2018                                                ** 
R_NET_MIG_2018                                                    ***
Median_Household_Income_2018                                      ***
Unemployment_change                                                  
Population_percent_change                                         ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.196 on 2323 degrees of freedom
Multiple R-squared:  0.7271,    Adjusted R-squared:  0.7262 
F-statistic:   884 on 7 and 2323 DF,  p-value: < 2.2e-16

And the sorted coefs. NB: I didn’t sort by absolute value here, so they aren’t in order of magnitude of effect.

sort(coef(linreg))
                                     Median_Household_Income_2018 
                                                       -3.4325582 
                                                   R_NET_MIG_2018 
                                                       -0.6083822 
                                              Unemployment_change 
                                                       -0.1191027 
                                               R_NATURAL_INC_2018 
                                                        0.2864007 
                                        Population_percent_change 
                                                        0.4354999 
                                           Unemployment_rate_2018 
                                                        1.2675679 
`Percent of adults with less than a high school diploma, 2014-18` 
                                                        1.6953941 
                                                      (Intercept) 
                                                       15.1993565 

How does it perform on the test set? Manually calculating Adj. R2 on test set

1 - (sum((y_test - add_predictions(X_test_sc, linreg)$pred)^2) / (dim(X_test_sc)[1] - dim(X_test_sc)[2] - 1)) / (sum((y_test - mean(y_test))^2) / (dim(X_test_sc)[1] - 1))
[1] 0.7005477

Now to get predictions on the entire scaled dataset (for mapping)

X_sc <- add_predictions(X_sc, linreg)

Mapping the error

palette(rainbow(50, start = 0, end = 0.3, rev = TRUE))
offset <- abs(min(X_sc$pred))
regions <- inner_join(county.fips, mutate(big_slice, pred = X_sc$pred), by = c("fips" = "FIPS"))
map('county', region = regions$polyname, exact=TRUE, fill = TRUE, col = (regions$pred + offset), lwd = 0.2)

FIPS code 46102 refers to Oglala Lakota County, which doesn’t appear in this fips directory due to a name change from Shannon County in 2014 which also changed the FIPS code.

filter(county.fips, polyname == "south dakota,shannon" | fips == 46102)

Plotting the predictions against actual to visually evaluate the model. I’d love to use a diverging palette centered on 0 here, but I’m moving on for the sake of time.

ggplot(data = X_sc) + 
  geom_point(mapping = aes(x = y, y = pred, color = (y - pred)))

Now let’s look at a model accounting for interactions

linreg2 <- lm(y_train ~ Unemployment_rate_2018 * `Percent of adults with less than a high school diploma, 2014-18` * R_NATURAL_INC_2018 * R_NET_MIG_2018 * Median_Household_Income_2018 * Unemployment_change * Population_percent_change, data = X_train_sc)
#summary(linreg2)

So that summary ended up being kind of huge, but hey! Significantly higher R^2 values and still a ridiculously small p-value. Good stuff. Summary commented out for cleanliness. Mapping the error for this one - still need to get better at working with palettes to have this be on the same scale as the previous map.

regions2 <- inner_join(county.fips, mutate(big_slice, pred = add_predictions(X_sc, linreg2)$pred), by = c("fips" = "FIPS"))
offset2 <- abs(min(regions$pred))
map('county', region = regions2$polyname, exact=TRUE, fill = TRUE, col = (regions$pred + offset2), lwd = 0.2)

And the test R^2 for linreg2

1 - (sum((y_test - add_predictions(X_test_sc, linreg2)$pred)^2) / (dim(X_test_sc)[1] - dim(X_test_sc)[2] - 1)) / (sum((y_test - mean(y_test))^2) / (dim(X_test_sc)[1] - 1))
[1] 0.7716487

Testing R^2 has improved as well, though not by as much, which is to be expected. Plotting the predicted against actual again for the interaction mode. Same caveat with the color palette as the similar plot above.

ggplot(data = add_predictions(X_sc, linreg2)) + 
  geom_point(mapping = aes(x = y, y = pred, color = (y - pred)))

And plotting error against actual

ggplot(data = add_predictions(X_sc, linreg2)) + 
  geom_point(alpha = 0.2, mapping = aes(x = y, y = (y - pred))) + 
  geom_ref_line(h = 0, colour = "red")

The errors visibly curve upwards at higher values. Investigating some of the worst errors. Still getting the hang of pipes.

big_slice %>% mutate( ErrorPoly = add_predictions(X_sc, linreg2)$pred - y) %>% arrange(desc(ErrorPoly))

Worth noting that if I did this correctly (not an insignificant “if”) my highest errors are quite different than the original Python ones. Strange and worth looking into.

Now the main event - Kmeans! Using the Lloyd algorithm to match with the version I created in sklearn.

(clustering <- kmeans(select(X_sc, !pred), centers = 8, iter.max = 1000, nstart = 100, algorithm = "Lloyd"))
K-means clustering with 8 clusters of sizes 540, 197, 555, 479, 194, 691, 200, 252

Cluster means:
  Percent of adults with less than a high school diploma, 2014-18
1                                                      0.50434023
2                                                      1.55167945
3                                                      0.02795914
4                                                     -0.74836368
5                                                     -0.32018264
6                                                     -0.39873646
7                                                      1.43060433
8                                                     -0.93149162
  R_NATURAL_INC_2018 R_NET_MIG_2018 Unemployment_rate_2018
1         -0.6263823      0.3595697              0.3104808
2         -0.2309517     -0.8658475              2.1137316
3         -0.6755405     -0.4238099              0.5229862
4          0.0676130     -0.6335739             -0.8609722
5          0.7051512      1.8549927             -0.4078850
6          0.1316757      0.3582573             -0.2998739
7          1.5197683     -0.7205129             -0.1399658
8          0.8441239      0.2856057             -0.6806658
  Median_Household_Income_2018 Unemployment_change Population_percent_change
1                   -0.6308310          -1.2286659               -0.21441392
2                   -1.1651410          -0.5631025               -0.84830246
3                   -0.5067658           0.2504223               -0.64959653
4                    0.1106185           1.1713470               -0.42662892
5                    0.8471009          -0.1438088                2.32917032
6                    0.2096499          -0.0819266                0.26733050
7                   -0.3345929           0.5519030                0.08145902
8                    2.1740270           0.3825758                0.87786462

Clustering vector:
   [1] 6 5 2 1 1 2 1 3 1 1 1 1 2 1 1 6 1 1 1 3 1 1 3 2 1 6 1 1 1 7 1 2 1 1 6
  [36] 1 6 1 3 1 5 5 2 2 6 1 1 6 3 1 6 1 2 1 3 1 1 6 8 2 1 1 6 1 2 2 1 2 3 6
  [71] 1 6 6 1 5 1 2 6 5 2 6 2 1 3 1 5 6 1 6 6 2 3 3 3 3 3 3 6 6 3 3 3 3 3 6
 [106] 3 1 6 6 6 7 6 3 6 3 2 3 7 3 3 2 3 3 3 6 7 1 4 2 3 3 3 3 3 3 2 3 3 3 6
 [141] 3 4 1 2 5 3 3 7 7 1 3 3 1 5 3 3 7 8 3 1 1 1 2 8 1 8 2 2 6 2 6 2 2 1 1
 [176] 7 2 8 1 6 2 2 6 7 8 6 8 8 1 5 6 5 7 8 8 1 6 8 8 8 8 1 1 1 8 8 1 2 1 1
 [211] 2 1 8 6 1 5 6 8 5 4 3 8 8 5 4 8 3 1 3 5 1 5 1 8 8 8 5 1 8 8 8 5 6 1 6
 [246] 8 6 4 6 8 5 1 6 4 6 5 6 6 6 7 3 8 5 4 8 7 6 4 3 8 5 5 8 4 8 6 4 5 4 8
 [281] 6 6 8 6 6 8 6 6 6 5 8 6 6 6 1 6 6 1 5 1 5 5 6 1 1 6 6 5 3 1 1 1 1 1 7
 [316] 7 1 1 5 1 5 3 1 1 5 5 6 1 6 1 5 1 6 6 4 5 5 1 5 5 6 5 6 5 1 5 5 5 5 6
 [351] 5 1 1 3 1 6 5 1 1 1 7 2 1 6 5 6 2 1 1 1 1 3 5 6 2 1 3 6 1 6 6 1 6 7 1
 [386] 5 6 2 6 7 8 1 7 5 1 5 1 1 3 5 1 6 1 1 1 6 1 7 5 1 1 7 1 8 1 5 1 8 1 3
 [421] 6 1 1 1 8 1 5 2 1 8 1 1 5 6 1 5 1 1 2 1 1 6 1 7 1 6 7 1 5 6 6 1 1 2 6
 [456] 2 1 3 2 6 1 6 2 6 6 5 1 5 1 6 6 6 1 3 1 2 1 2 7 6 1 1 1 1 1 1 2 3 1 7
 [491] 2 2 2 3 1 2 1 2 1 1 2 5 1 1 5 1 1 1 1 2 2 6 7 1 1 1 1 5 1 6 6 1 6 8 5
 [526] 5 8 6 4 6 5 6 7 7 1 6 6 8 6 6 7 1 5 7 5 6 6 3 7 7 7 4 6 7 6 7 1 8 6 5
 [561] 6 4 2 3 6 4 3 3 3 3 6 3 3 3 6 3 6 3 6 6 3 7 8 3 3 6 3 3 3 3 3 3 8 3 3
 [596] 2 3 3 3 3 6 3 3 3 2 8 3 8 3 8 3 3 3 3 3 3 8 4 3 3 6 3 3 3 3 4 3 8 3 3
 [631] 6 6 3 3 4 3 3 2 6 3 3 3 3 3 4 3 3 6 3 3 3 3 3 6 4 4 3 3 3 8 3 1 4 7 6
 [666] 6 6 1 8 6 6 1 6 6 6 1 7 6 6 6 1 6 6 1 6 1 6 6 4 1 3 8 8 6 8 1 1 6 6 6
 [701] 6 6 1 8 4 6 7 3 1 1 1 6 6 6 1 6 6 6 1 1 1 1 1 6 6 6 6 6 3 6 1 6 1 6 1
 [736] 6 6 1 6 3 6 6 6 3 6 1 6 1 6 8 1 1 6 6 6 4 4 6 6 4 4 4 4 8 4 7 4 4 4 4
 [771] 4 4 4 4 6 4 4 4 7 5 7 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 6 4 4 4 4 6 6
 [806] 6 8 4 4 4 3 8 4 4 4 8 4 6 4 4 4 4 4 4 6 4 4 4 4 4 4 8 4 4 4 4 6 4 8 4
 [841] 4 4 4 4 6 8 4 4 4 4 4 6 4 4 3 4 3 4 4 3 4 6 4 3 3 4 4 4 4 4 4 4 6 4 4
 [876] 6 6 4 3 4 4 7 7 6 7 4 3 7 7 4 3 7 3 4 7 4 4 4 4 8 7 4 4 3 4 8 4 3 4 4
 [911] 4 4 4 4 8 4 3 6 4 4 3 4 4 6 4 4 4 4 8 4 4 4 4 4 4 4 4 4 4 4 6 7 4 4 4
 [946] 4 4 4 7 4 4 4 4 4 4 4 3 3 7 1 1 6 3 1 2 2 8 6 3 6 3 2 1 6 1 3 6 6 3 1
 [981] 2 1 7 6 2 2 3 1 6 1 2 1 6 1 2 6 2 1 1 6
 [ reached getOption("max.print") -- omitted 2108 entries ]

Within cluster sum of squares by cluster:
[1] 1576.3545 1099.2593 1408.4598 1144.5249 1262.7983 1552.7751 1084.8146
[8]  893.7738
 (between_SS / total_SS =  53.2 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"    
[5] "tot.withinss" "betweenss"    "size"         "iter"        
[9] "ifault"      

Checking the attributes

attributes(clustering)
$names
[1] "cluster"      "centers"      "totss"        "withinss"    
[5] "tot.withinss" "betweenss"    "size"         "iter"        
[9] "ifault"      

$class
[1] "kmeans"
attributes(clustering)$centers
NULL

I am still confused by attributes in R…

---
title: "County Cluster R"
output: html_notebook
---

This is a rewrite of my County Cluster notebook - originally written in Python in Jupyter Notebook - using R. The code may not be pretty, buy my goal is simply to practice R and demonstrate basic competence. 

Loading packages, setting random seed
```{r}
library(tidyverse)
library(modelr)
library(maps)
library(mapproj)
library(htmlwidgets)
set.seed(123)
```

Read in datasets
```{r}
ed_data <- read_csv("../data/Education.csv", locale = locale(encoding = "Latin1"))
pop_data <- read_csv("../data/PopulationEstimates.csv", locale = locale(encoding = "Latin1"))
pov_data <- read_csv("../data/PovertyEstimates.csv", locale = locale(encoding = "Latin1"))
unm_data <- read_csv("../data/Unemployment.csv", locale = locale(encoding = "Latin1"))
```
Taking a look at the education data
```{r}
head(ed_data)
```
And the ed_data dimensions
```{r}
dim(ed_data)
```
Now looking at population data - it had a problem when reading in, we'll have to interrogate that more later
```{r}
head(pop_data)
```
pop_data dimensions
```{r}
dim(pop_data)
```
And now poverty data
```{r}
head(pov_data)
```
poverty dimensions
```{r}
dim(pov_data)
```
And finally, unemployment data
```{r}
head(unm_data)
```
and unemployment data dimensions
```{r}
dim(unm_data)
```
A quick look at the summaries of each
```{r}
#summary(ed_data)
```
```{r}
#summary(pop_data)
```
```{r}
#summary(pov_data)
```
```{r}
#summary(unm_data)
```
So it turns out that while summary() in R returns much the same information as .describe() does in pandas, it does it in a really ungainly format. I don't think I'll be doing that again. I've commented out the summaries to avoid clogging the notebook. 
At this point in pandas I listed out the data types in each dataframe, but here those have already been displayed on the head(). I also loaded in a shape file with the geometries of the counties, but this was ultimately only used for plotting and the maps package in R already covers US county mapping, so I won't be using the shape file for this. 

First, a tiny bit of housekeeping - making all the FIPS columns called just "FIPS".
```{r}
ed_data <- rename(ed_data, FIPS = "FIPS Code")
pov_data <- rename(pov_data, FIPS = FIPStxt)
```

Before joining, need to check that FIPS are actually unique
```{r}
dim(distinct(select(ed_data, FIPS)))[1] == dim(ed_data)[1]
```
```{r}
dim(distinct(select(pop_data, FIPS)))[1] == dim(pop_data)[1]
```
```{r}
dim(distinct(select(pov_data, FIPS)))[1] == dim(pov_data)[1]
```
```{r}
dim(distinct(select(unm_data, FIPS)))[1] == dim(unm_data)[1]
```
All good on that front. Of the four dataframes, three read in FIPS as dbl while pop_data read it in as chr. While the latter is more technically correct, I'll convert pop_data FIPS to dbl so all four are on the same page when it comes to leading zeros and data types. 
```{r}
(pop_data <- mutate(pop_data, across(FIPS, as.double)))
```
And now to join them all on FIPS. And yes, the name of big_data is tongue-in-cheek.
```{r}
big_data <- ed_data %>%
  full_join(pop_data, by = c("FIPS")) %>%
  full_join(pov_data, by = c("FIPS")) %>%
  full_join(unm_data, by = c("FIPS"))
```
Let's take a look at the result
```{r}
head(big_data)
```
```{r}
dim(big_data)
```
and now let's see how many nulls there are
```{r}
sort(colSums(is.na(big_data)))
```
All columns have at least one null (I suspect this is due to the parsing problem from pop_data), a large number have <20, another significant group have 50-100, and a handful have 140-150 or outright majority nulls. On closer inspection, the outright majority null columns are state-level data which is not necessary for my analysis. Many of the other nulls may be the result of state-levels rows.
Before anything else, I'll save a copy of the full data
```{r}
write_csv(big_data, "../data/outer_join_R.csv")
```
And now to clean it up. First to take out the mostly null columns
```{r}
(big_data <- select(big_data, -c(POV04_2018, CI90LB04_2018, CI90UB04_2018, PCTPOV04_2018, CI90LB04P_2018, CI90UB04P_2018)))
```
Now the number of rows with nulls is much more reasonable
```{r}
sum(!complete.cases(big_data))
```
Let's see if my suspicion there's a fully null row is correct
```{r}
filter(big_data, apply(is.na(big_data), 1, all))
```
Getting rid of the fully null row
```{r}
big_data <- filter(big_data, !apply(is.na(big_data), 1, all))
```
Now let's look at the remaining rows with at least one null
```{r}
filter(big_data, apply(is.na(big_data), 1, any))
```
That's mostly Puerto Rico and Alaska, as well as the summary rows for states and the entire country. 
Here's the rows with at least one null which DON'T meet any of the criteria I just listed.
```{r}
filter(big_data, apply(is.na(big_data), 1, any) & !(State.x %in% c("AK", "PR")) & (FIPS %% 1000 != 0))
```
At this point in the original pandas notebook I broke to review external research to better understand what was going on with these counties (and county equivalents). I decided to narrow down to just columns I thought I might actually use before cleaning further, as the time component complicated cleaning some counties pretty severely. 
```{r}
(big_slice <- select(big_data, FIPS, State.x, "Area name", '2013 Rural-urban Continuum Code', '2013 Urban Influence Code', "Percent of adults with less than a high school diploma, 2014-18", 'Percent of adults with a high school diploma only, 2014-18', "Percent of adults completing some college or associate's degree, 2014-18", "Percent of adults with a bachelor's degree or higher, 2014-18", 'Economic_typology_2015', 'POP_ESTIMATE_2010', 'POP_ESTIMATE_2018', 'R_NATURAL_INC_2018', 'R_NET_MIG_2018', 'PCTPOVALL_2018', 'Unemployment_rate_2010', 'Unemployment_rate_2018', 'Median_Household_Income_2018'))
    
```
Now I'll get rid of Alaska, Hawaii, Puerto Rico, and the summary rows
```{r}
(big_slice <- filter(big_slice, !(State.x %in% c("AK", "PR", "HI")) & (FIPS %% 1000 != 0)))
```
Now let's see what nulls remain
```{r}
filter(big_slice, apply(is.na(big_slice), 1, any))
```
Based on external research, I think I can drop all three of these without losing much
```{r}
(big_slice <- drop_na(big_slice))
```
Now to finish the cleaning. I'll make FIPS an int in R instead of a chr for mapping reasons which will come up later
```{r}
(big_slice <- mutate(big_slice, across(FIPS, as.integer)))
```
The Rural-urban codes should be ints as well
```{r}
(big_slice <- mutate(big_slice, across("2013 Rural-urban Continuum Code":"2013 Urban Influence Code", as.integer)))
```
Economic typology should really be a chr
```{r}
(big_slice <- mutate(big_slice, across(Economic_typology_2015, as.character)))
```
Population estimates are currently doubles but should be integers
```{r}
(big_slice <- mutate(big_slice, across(POP_ESTIMATE_2010:POP_ESTIMATE_2018, as.integer)))
```
And last but not least for type changes, median household income is currently a chr with dollar signs and thousand separators, neither of which we want
```{r}
big_slice$Median_Household_Income_2018 <- str_replace_all(big_slice$Median_Household_Income_2018, "[[:punct:]/$]", "")
```
```{r}
(big_slice <- mutate(big_slice, across(Median_Household_Income_2018, as.integer)))
```
Much better. Now I can create some new variables
```{r}
(big_slice <- mutate(big_slice,
  Unemployment_change = Unemployment_rate_2018 - Unemployment_rate_2010,
  Population_percent_change = (POP_ESTIMATE_2018 - POP_ESTIMATE_2010) / POP_ESTIMATE_2010
))
```
And also one-hot-encode the Economic Typology
```{r}
big_slice %>% 
  mutate(ones = 1) %>% 
  pivot_wider(
  names_from = Economic_typology_2015,
  values_from = ones,
  names_prefix = "Econ_Typology_",
  names_sort = TRUE,
  values_fill = 0,
  values_fn = as.integer)
```
Saving a copy of the modeling-ready data
```{r}
write_csv(big_slice, "../data/processed_R.csv")
```
Let's specify the X and y to use for modeling - more features may be used later
```{r}
(X <- select(big_slice, 6, 13, 14, 17, 18, 19, 20))
y <- big_slice$PCTPOVALL_2018
```
Train test split
```{r}
train_len <- as.integer(0.75 * dim(X)[1])
train_test_split <- sample(c(rep(0, train_len), rep(1, dim(X)[1] - train_len)))
X_train <- X[train_test_split == 0, ]
X_test <- X[train_test_split == 1, ]
y_train <- y[train_test_split == 0]
y_test <- y[train_test_split == 1]
```
Scaling the data
```{r}
X_train_sc <- scale(X_train)
```
Can we get that scale back?
```{r}
train_scale <- attributes(X_train_sc)$`scaled:scale`
train_centers <- attributes(X_train_sc)$`scaled:center`
```
And now to apply that to X_test
```{r}
X_test_sc <- as_tibble(scale(X_test, center = train_centers, scale = train_scale))
X_sc <- as_tibble(scale(X, center = train_centers, scale = train_scale))
X_train_sc <- as_tibble(X_train_sc)
```
Now a linear model
```{r}
linreg <- lm(y_train ~ Unemployment_rate_2018 + `Percent of adults with less than a high school diploma, 2014-18` + R_NATURAL_INC_2018 + R_NET_MIG_2018 + Median_Household_Income_2018 + Unemployment_change + Population_percent_change, data = X_train_sc)
```
Looking at the model summary - note that the R2 isn't great, but not bad at all
```{r}
summary(linreg)
```
And the sorted coefs. NB: I didn't sort by absolute value here, so they aren't in order of magnitude of effect.
```{r}
sort(coef(linreg))
```
How does it perform on the test set? Manually calculating Adj. R2 on test set
```{r}
1 - (sum((y_test - add_predictions(X_test_sc, linreg)$pred)^2) / (dim(X_test_sc)[1] - dim(X_test_sc)[2] - 1)) / (sum((y_test - mean(y_test))^2) / (dim(X_test_sc)[1] - 1))
```
Now to get predictions on the entire scaled dataset (for mapping)
```{r}
X_sc <- add_predictions(X_sc, linreg)
```
Mapping the error
```{r}
palette(rainbow(50, start = 0, end = 0.3, rev = TRUE))
offset <- abs(min(X_sc$pred))
regions <- inner_join(county.fips, mutate(big_slice, pred = X_sc$pred), by = c("fips" = "FIPS"))
map('county', region = regions$polyname, exact=TRUE, fill = TRUE, col = (regions$pred + offset), lwd = 0.2)
```
FIPS code 46102 refers to Oglala Lakota County, which doesn't appear in this fips directory due to a name change from Shannon County in 2014 which also changed the FIPS code. 
```{r}
filter(county.fips, polyname == "south dakota,shannon" | fips == 46102)
```

Plotting the predictions against actual to visually evaluate the model. I'd love to use a diverging palette centered on 0 here, but I'm moving on for the sake of time. 
```{r}
ggplot(data = X_sc) + 
  geom_point(mapping = aes(x = y, y = pred, color = (y - pred)))
```
Now let's look at a model accounting for interactions
```{r}
linreg2 <- lm(y_train ~ Unemployment_rate_2018 * `Percent of adults with less than a high school diploma, 2014-18` * R_NATURAL_INC_2018 * R_NET_MIG_2018 * Median_Household_Income_2018 * Unemployment_change * Population_percent_change, data = X_train_sc)
#summary(linreg2)
```
So that summary ended up being kind of huge, but hey! Significantly higher R^2 values and still a ridiculously small p-value. Good stuff. Summary commented out for cleanliness.
Mapping the error for this one - still need to get better at working with palettes to have this be on the same scale as the previous map. 
```{r}
regions2 <- inner_join(county.fips, mutate(big_slice, pred = add_predictions(X_sc, linreg2)$pred), by = c("fips" = "FIPS"))
offset2 <- abs(min(regions$pred))
map('county', region = regions2$polyname, exact=TRUE, fill = TRUE, col = (regions$pred + offset2), lwd = 0.2)
```
And the test R^2 for linreg2
```{r}
1 - (sum((y_test - add_predictions(X_test_sc, linreg2)$pred)^2) / (dim(X_test_sc)[1] - dim(X_test_sc)[2] - 1)) / (sum((y_test - mean(y_test))^2) / (dim(X_test_sc)[1] - 1))
```
Testing R^2 has improved as well, though not by as much, which is to be expected. Plotting the predicted against actual again for the interaction mode. Same caveat with the color palette as the similar plot above.
```{r}
ggplot(data = add_predictions(X_sc, linreg2)) + 
  geom_point(mapping = aes(x = y, y = pred, color = (y - pred)))
```
And plotting error against actual
```{r}
ggplot(data = add_predictions(X_sc, linreg2)) + 
  geom_point(alpha = 0.2, mapping = aes(x = y, y = (y - pred))) + 
  geom_ref_line(h = 0, colour = "red")
```
The errors visibly curve upwards at higher values. 
Investigating some of the worst errors. Still getting the hang of pipes. 
```{r}
big_slice %>% mutate( ErrorPoly = add_predictions(X_sc, linreg2)$pred - y) %>% arrange(desc(ErrorPoly))
```
Worth noting that if I did this correctly (not an insignificant "if") my highest errors are quite different than the original Python ones. Strange and worth looking into. 

Now the main event - Kmeans! Using the Lloyd algorithm to match with the version I created in sklearn. 
```{r}
(clustering <- kmeans(select(X_sc, !pred), centers = 8, iter.max = 1000, nstart = 100, algorithm = "Lloyd"))
```
Checking the attributes
```{r}
attributes(clustering)
```
```{r}
attributes(clustering)$centers
```
I am still confused by attributes in R...